Update 03/SEP/2024 / edit 05/AUG:
👋 Looking for a Text Encoder for Flux.1 (or SD3, SDXL, SD, ...) to replace CLIP-L? 👀
You'll generally want the "TE-only" .safetensors:
- 👉 The "TEXT" model has superior prompt following, especially for text, but also for other details. DOWNLOAD
- 👉 The "SMOOTH" model can sometimes** have better details (when there's no text in the image). DOWNLOAD
- The "GmP" initial fine-tune is deprecated / inferior to the above models. Still, you can DOWNLOAD it.
**: The "TEXT" model is the best for text. Full stop. But whether the "SMOOTH" model is better for your (text-free) scenario than the "TEXT" model really depends on the specific prompt. It might also be the case that the "TEXT" model leads to images that you prefer over "SMOOTH"; the only way to know is to experiment with both.
🤓👨💻 In general (because we're not limited to text-to-image generative AI), I provide four versions / downloads:
- Text encoder only .safetensors.
- Full model .safetensors.
- State_dict pickle.
- Full model pickle (can be used as-is with "import clip" -> clip.load() after bypassing SHA checksum verification).
The TEXT model has a modality gap of 0.80 (OpenAI pre-trained: 0.82).
- Trained with high temperature of 0.1 + tinkering.
- ImageNet/ObjectNet accuracy ~0.91 for both "SMOOTH" and "TEXT" models (pre-trained: ~0.84).
- The models (this plot = "TEXT" model on MSCOCO) are also golden retrievers: 🥰🐕
Update 11/AUG/2024:
New Best-Performing CLIP ViT-L/14 'GmP-smooth' model added (simply download the files named BEST!):
Or just create a fine-tune yourself: https://github.com/zer0int/CLIP-fine-tune
How?
- Geometric Parametrization (GmP) (same as before)
- Activation Value manipulation for 'adverb neuron' (same as before)
- NEW: Custom loss function with label smoothing!
- For in-depth details, see my GitHub. 🤗
A fine-tune of OpenAI / CLIP ViT-L/14 that has an unprecedented ImageNet/ObjectNet accuracy of ~0.90 (original pre-trained model / OpenAI's CLIP: ~0.85)**.
Made possible with Geometric Parametrization (GmP):
"Normal" CLIP MLP (multi-layer perceptron):
(mlp): Sequential(
|-(c_fc): Linear(in_features=1024, out_features=4096, bias=True)
| (gelu): QuickGELU()
|-}-(c_proj): Linear(in_features=4096, out_features=1024, bias=True)
| |
| |-- visual.transformer.resblocks.0.mlp.c_fc.weight
| |-- visual.transformer.resblocks.0.mlp.c_fc.bias
|
|---- visual.transformer.resblocks.0.mlp.c_proj.weight
|---- visual.transformer.resblocks.0.mlp.c_proj.bias
GmP CLIP MLP:
Weight decomposition into:
- radial component 'r' as norm of pre-trained weights
- angular component 'theta' as normalized direction
-> preserves weight vectors' directionality and magnitude
(mlp): Sequential(
|-(c_fc): GeometricLinear()
| (gelu): QuickGELU()
|-}-(c_proj): GeometricLinear()
| |
| |-- visual.transformer.resblocks.0.mlp.c_fc.r
| |-- visual.transformer.resblocks.0.mlp.c_fc.theta
| |-- visual.transformer.resblocks.0.mlp.c_fc.bias
|
|---- visual.transformer.resblocks.0.mlp.c_proj.r
|---- visual.transformer.resblocks.0.mlp.c_proj.theta
|---- visual.transformer.resblocks.0.mlp.c_proj.bias
(Same thing for [text] transformer.resblocks)
✅ The model / state_dict I am sharing was converted back to .weight after fine-tuning - alas, it can be used in the same manner as any state_dict, e.g. for use with ComfyUI as the SDXL / SD3 Text Encoder! 🤗
- ** For details on training and those numbers / the eval, please see https://github.com/zer0int/CLIP-fine-tune
- -> You can use "exp-acts-ft-finetune-OpenAI-CLIP-ViT-L-14-GmP-manipulate-neurons.py" to replicate my exact model fine-tune.
Pre-trained CLIP model by OpenAI, License: MIT License
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